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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Tuba, Eva Strumberger, Ivana Bacanin, Nebojsa Zivkovic, Dejan Tuba, Milan Singidunum Univ Fac Informat & Comp Belgrade Serbia Singidunum Univ Belgrade Serbia

ISBN: (纸本)9781728121536

Digital images are part of numerous applications and they are used for various tasks. One of the important research topics in digital image processing is image fusion that aims to create more informative image by fusing two or more images from different sources or different points of view. The fused image is obtained as a linear combination of source images. Finding the optimal image fusion parameters, i.e. scaling factors, represents a hard optimization problem and in this paper we proposed brain storm optimization algorithm for solving it. Image fusion is done in frequency domain and the scaling factors for DCT coefficients of the source images are searched for. The proposed brain storm optimization method was tested with thermal and visual images. Standard benchmark dataset from TNO thermal image fusion was used for determining the quality of the proposed method. By comparing the obtained results it can be concluded that the proposed brain storm optimization method outperformed particle swarm optimization method, as well as several standard image fusion methods such as gradient pyramid, Laplacian pyramid, ratio of Laplacian pyramid and shift invariant discrete wavelet transform, in terms of four quality metrics including entropy, QABF, LABF and NABF.

关键词： digital image processing image fusion thermal images brain storm optimization algorithm swarm intelligence optimization

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brain storm optimization algorithm in Objective Space

Brain Storm Optimization Algorithm in Objective Space

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IEEE Congress on Evolutionary Computation (CEC)

作者： Shi, Yuhui Xian Jiaotong Liverpool Univ Dept Elect & Elect Engn Suzhou Peoples R China

ISBN: (纸本)9781479974924

brain storm optimization algorithm is a newly proposed swarm intelligence algorithm, which has two main operations, i.e., convergent operation and divergent operation. In the original brain storm optimization algorithm, a clustering algorithm is utilized to cluster individuals into clusters as the convergent operation which is time consuming because of distance calculation during clustering. In this paper, a new convergent operation is proposed to be implemented in the 1-dimensional objective space instead of in the solution space. As a consequence, its computation time will depend on only the population size, not the problem dimension, therefore, a big computation time saving can be obtained which makes it have good scalability. Experimental results demonstrate the effectiveness and efficiency of the proposed brain storm optimization algorithm in objective space.

关键词： brain storm optimization algorithm objective space swarm intelligence component

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brain storm optimization algorithm based on adaptive inertial selection strategy for the RCPSP

Brain Storm Optimization Algorithm based on adaptive inertia...

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Chinese Automation Congress (CAC)

作者： Wu, Yali Wang, XiaoPeng Li, GuoTing Lu, ATing Xian Univ Technol Sch Automat & Informat Engn Xian Peoples R China

ISBN: (纸本)9781728140940

RCPSP(Resource-constrained Project Scheduling Problem)that has been proved to be a NP-hard problem is a kind of combination optimization problem. With the problem scale becoming larger and larger, the problem will become more difficult to solve. A discussion mechanism based on brain storm optimization (DMBSO) for the RCPSP was presented. According to the characteristics of the RCPSP and the problem of poor accuracy and slow convergence in later period of algorithm. We proposed an adaptive inertial selection strategy based on DMBSO (AD-DMBSO) to solve the RCPSP. The algorithm can control the probability of inter-group and intragroup discussion by adjusting the probability of population grouping strategy, which can strengthen the global search in the early stage, and enhance the local search in the later stage. We test AD-DMBSO algorithm on four sets of problem from PSPLIB. The experimental result showed that the proposed algorithm is superior to the DMBSO and particle swarm optimization (PSO) algorithm in solving the local optimum problem and converging to better optimal values. The excellent performance of the AD-DMBSO algorithm shows its great potential in solving the RCPSP.

关键词： brain storm optimization algorithm discussion mechanism adaptive inertial selection projects scheduling problem

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brain storm optimization algorithm in Objective Space for Reliability-Redundancy Allocation Problem

Brain Storm Optimization Algorithm in Objective Space for Re...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Nath, Rahul Rauniyar, Amit Muhuri, Pranab K. South Asian Univ Dept Comp Sci New Delhi 110021 India

ISBN: (纸本)9781728121536

This paper deals with one of the well-studied research problem known as reliability redundancy allocation problem (RRAP). RRAP is finds an optimal balance in selecting reliability of the components and the number of redundant components considering the effect of cost, weight, and volume on the overall system as constraints. Reliability is a very crucial issue in every engineering application. In this paper, our study aims to find the optimal layout for RRAP using brain storm optimization algorithm in objective space (BSO-OS). BSO is inspired by human cognitive skills. The convergent and divergent operation to reduce the search space and diversification of population are the prime objective in BSO. Results are compared by solving the RRAP with the self-organizing migrating algorithm (SOMA) and the genetic algorithm (GA). It is found that BSO-OS outperform others in terms of time complexity providing similar results.

关键词： Reliability redundancy allocation problem brain storm optimization algorithm Objective space Self organizing migrating algorithm Genetic algorithm

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Elastic parameter inversion problem based on brain storm optimization algorithm

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MEMETIC COMPUTING 2019年第2期11卷 143-153页

作者： Yan, Xuesong Zhu, Zhixin Wu, Qinghua Gong, Wenyin Wang, Ling China Univ Geosci Sch Comp Sci Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China Wuhan Inst Technol Fac Comp Sci & Engn Wuhan 430205 Hubei Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

The pre-stack Amplitude Variation with Offset (AVO) elastic parameter inversion technique combined with an intelligent optimization algorithm provides a more effective identification method for oil and gas exploration. However, biological evolution-based optimization algorithms, such as genetic algorithm, generally suffer problems such as premature convergence and high probability of becoming trapped in a local optimum, and these problems lead to unsatisfactory inversion results. To solve the above problems, this paper proposes a swarm-intelligence-based brain storm optimization algorithm, which is more suitable for solving the inversion problem of pre-stack AVO elastic parameters. The algorithm employs a specific initialization strategy for Aki and Rechard's approximation equation, which is used in the inversion process, to produce a smoother initialization parameter curve. Multiple experiments prove that the correlation coefficients of the elastic parameters obtained by inversion are high, while the inversion accuracy is improved significantly.

关键词： brain storm optimization algorithm Pre-stack AVO Elastic parameter inversion Correlation coefficient

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Adaptive CCR-ELM with variable-length brain storm optimization algorithm for class-imbalance learning

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NATURAL COMPUTING 2021年第1期20卷 11-22页

作者： Cheng, Jian Chen, Jingjing Guo, Yi-nan Cheng, Shi Yang, Linkai Zhang, Pei China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221008 Jiangsu Peoples R China

Class-specific cost regulation extreme learning machine (CCR-ELM) can effectively deal with the class imbalance problems. However, its key parameters, including the number of hidden nodes, the input weights, the biases and the tradeoff factors are normally generated randomly or preset by human. Moreover, the number of input weights and biases depend on the size of hidden layer. Inappropriate quantity of hidden nodes may lead to the useless or redundant neuron nodes, and make the whole structure complex, even cause the worse generalization and unstable classification performances. Based on this, an adaptive CCR-ELM with variable-length brain storm optimization algorithm is proposed for the class imbalance learning. Each individual consists of all above parameters of CCR-ELM and its length varies with the number of hidden nodes. A novel mergence operator is presented to incorporate two parent individuals with different length and generate a new individual. The experimental results for nine imbalance datasets show that variable-length brain storm optimization algorithm can find better parameters of CCR-ELM, resulting in the better classification accuracy than other evolutionary optimization algorithms, such as GA, PSO, and VPSO. In addition, the classification performance of the proposed adaptive algorithm is relatively stable under varied imbalance ratios. Applying the proposed algorithm in the fault diagnosis of conveyor belt also proves that ACCR-ELM with VLen-BSO has the better classification performances.

关键词： Variable-length Adaptive brain storm optimization algorithm Class-specific cost regulation extreme learning machine The class imbalance problems

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A modified brain storm optimization algorithm with a special operator to solve constrained optimization problems

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APPLIED INTELLIGENCE 2020年第12期50卷 4145-4161页

作者： Cervantes-Castillo, Adriana Mezura-Montes, Efren Univ Veracruz Artificial Intelligence Res Ctr Sebastian Camacho 5 Xalapa 91000 Veracruz Mexico

This paper presents a novel approach based on the combination of the Modified brain storm optimization algorithm (MBSO) with a simplified version of the Constraint Consensus method as special operator to solve constrained numerical optimization problems. Regarding the special operator, which aims to reach the feasible region of the search space, the consensus vector becomes the feasibility vector computed by the hardest constraint in turn for a current infeasible solution;then the operations to mix the other feasibility vectors are avoided. This new combined algorithm, named as MBSO-R+V, solves a suit of eighteen test problems in ten and thirty dimensions. From a set of experiments related to the location and frequency of application of the constraint consensus method within MBSO, a suitable design of the combined approach is presented. This proposal shows encouraging final results while being compared against state-of-the-art algorithms, showing that it is viable to add special operators to improve the capabilities of swarm-intelligence algorithms when dealing with continuous constrained search spaces.

关键词： brain storm optimization algorithm Constrained numerical optimization problems Constraint-consensus method Feasibility vectors epsilon-constrained method

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A Zoning Search-Based Multimodal Multi-Objective brain storm optimization algorithm for Multimodal Multi-Objective optimization

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algorithmS 2023年第7期16卷

作者： Fan, Jiajia Huang, Wentao Jiang, Qingchao Fan, Qinqin Shanghai Maritime Univ Logist Res Ctr Shanghai 201306 Peoples R China Shanghai Jiao Tong Univ Minist Educ Key Lab Control Power Transmiss & Convers Shanghai 200240 Peoples R China East China Univ Sci & Technol Minist Educ Key Lab Smart Mfg Energy Chem Shanghai 200237 Peoples R China

For multimodal multi-objective optimization problems (MMOPs), there are multiple equivalent Pareto optimal solutions in the decision space that are corresponding to the same objective value. Therefore, the main tasks of multimodal multi-objective optimization (MMO) are to find a high-quality PF approximation in the objective space and maintain the population diversity in the decision space. To achieve the above objectives, this article proposes a zoning search-based multimodal multi-objective brain storm optimization algorithm (ZS-MMBSO). At first, the search space segmentation method is employed to divide the search space into some sub-regions. Moreover, a novel individual generation strategy is incorporated into the multimodal multi-objective brain storm optimization algorithm, which can improve the search performance of the search engineering. The proposed algorithm is compared with five famous multimodal multi-objective evolutionary algorithms (MMOEAs) on IEEE CEC2019 MMOPs benchmark test suite. Experimental results indicate that the overall performance of the ZS-MMBSO is the best among all competitors.

关键词： brain storm optimization algorithm multimodal multi-objective optimization evolutionary computation zoning search

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Diameter Detection of Silicon Single Crystal Based on brain storm optimization algorithm Hough Transform 38

Diameter Detection of Silicon Single Crystal Based on Brain ...

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38th Chinese Control Conference (CCC)

作者： Kang, Lu Liu, Ding Xian Univ Technol Natl & Local Joint Engn Res Ctr Crystal Growth Eq Xian 710048 Peoples R China Shaanxi Key Lab Complex Syst Control & Intelligen Xian 710048 Peoples R China

ISBN: (纸本)9789881563972

In order to improve the speed and accuracy of silicon single crystal diameter detection. this paper proposes an ellipse detection method based on brain storm optimization algorithm Hough transform, this method uses brain storm optimization algorithm to search ellipse parameters that make the accumulator obtains the maximum value after using Hough transform, then the accurate measurement value of silicon single crystal diameter is obtained. The proposed algorithm is simulated on the complete ellipse with Gaussian white noise and silicon single crystal diameter detection engineering experiments, and the results are compared with other intelligent optimization algorithms. Experimental results show that the proposed algorithm can effectively improve the speed and accuracy of ellipse detection with Hough transform, thereby verify the feasibility and effectiveness of the proposed algorithm, and improve the speed and accuracy of silicon single crystal diameter detection.

关键词： Silicon single crystal Diameter detection Ellipse detection brain storm optimization algorithm Hough transform

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Wind Turbine Pitch Control Based on Error-based ADRC Approach Optimized by brain storm optimization algorithm 1

Wind Turbine Pitch Control Based on Error-based ADRC Approac...

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1st International Conference on Industrial Artificial Intelligence (IAI)

作者： Huang, Congzhi Yin, Yue North China Elect Power Univ Sch Control & Comp Engn Beijing Peoples R China

ISBN: (纸本)9781728135939

Most pitch controllers of wind turbines are PI controllers in practical application. However, due to the nonlinear characteristic of pitch control process and inaccurate modeling of wind turbine, it is inevitable to study new and more effective controllers. In this paper, an error-based ADRC approach is designed based on the simplified two-mass model of wind turbine. The total disturbances, including the internal model uncertainties, and external disturbances, can be estimated by the constructed extended state observer, and then rejected by the PD controller in real time. The two parameters in the controller, including the observer bandwidth, and the controller bandwidth, are optimized by brain storm optimization algorithm. The experiment is carried out by FAST software. The results show that the control performance of error-based ADRC approach is much better than that of PI controller.

关键词： wind turbine error-based ADRC brain storm optimization algorithm FAST

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